I am currently training a model using an azure ML pipeline that i build with sdk. I am trying to add cross-validation to my ml step. I have noticed that you can add this in the parameters when you configure the autoML. My dataset consists of 30% label 0 and 70% label 1.
My question is, does azure autoML stratify data when performing the cross-validation? If not i would have to do the split/stratify myself before passing it to autoML.
Auto ML can stratify the data when performing cross-validation. The following procedure needs to be followed to perform cross-validation
Create the workspace resource.
After giving all the details, click on create
Launch the Studio and go to AutoML and click on New Automated ML job
Upload the dataset from here and give the basic details required.
Dataset uploaded with some basic categories
After uploading dataset use that dataset for the prediction model performance
Here for prediction, we can choose the k-fold cross validation for validation type and number of cross validations as 5. There is no split we are performing. The model will perform according to the validation requirements.
Related
I developed a designer to implement regression models in azure machine learning studio. I have taken the data set pill and then split the data set into train and test in prescribed manner. When I am trying to implement the evaluation metrics and run the pipeline, it was showing a warning and error in the moment I called the dataset for the operation. I am bit confused, with the same implementation, when i tried to run with linear regression and it worked as shown in the image. If the same approach is used to implement logistic regression it was showing some warning and error in building the evaluation metrics.
the above success is in linear regression. When it comes to logistic regression it was showing the warning and error in pipeline.
Any help is appreciated.
Creating a sample pipeline with designer with mathematical format.
We need to create a compute instance.
Assign the compute instance and click on create
Now the import data warning will be removed. In the same manner, we will be getting similar error in other pills too.
Create a mathematical format. If not needed for your case, try to remove that math operation and give the remaining.
Assign the column set. Select any option according to the requirement.
Finally, we can find the pills which have no warning or error.
Is it possible to generate prediction intervals for time series forecasts when using a Azure AutoML trained models? Could we get the training errors out of the process and use them for bootstrapping?
You can generate forecast quantiles. See the following notebook for more details: https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb
I want to train the model with binary logistic regression model,with a dataset of 3000 data points. while creating the pipeline , it fails at the training model step.
Please help me in training the model with large dataset or retrain the model continuously.
Also Do pipelines have any limitation on the dataset? if so, what is the limit
I haven't seen there is a limitation for training dataset size. May I know how you do the pipeline? If you are using Azure Machine Learning Designer, could you please try the enterprise version? https://learn.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines#building-pipelines-with-the-designer
Also, I have attached a tutorial here for large data pipeline: https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-pipeline-batch-scoring-classification
I am creating Azure ML experienment to predict multiple values. but in azure ml we can not train a model to predict multiple values. my question is how to bring multiple trained models in single experienment and create webout put that gives me multiple prediction.
You would need to manually save the trained models (right click the module output and save to your workspace) from your training experiment and then manually create the predictive experiment unlike what is done in this document. https://learn.microsoft.com/en-us/azure/machine-learning/studio/walkthrough-5-publish-web-service
Regards,
Jaya
I created an ML pipeline with several transformers, including a StringIndexer which is used during training on the data labels.
I then store the resultant PipelineModel which will later be used for data preparation and prediction on a dataset which doesn't have labels.
The issue is that the created pipeline model's transform function cannot be applied to the new DataFrame, since it expects data labels to be available.
What am I missing?
How should this be done?
Note: My goal is to have a single pipeline (i.e. I'd like to keep the various transformations and ML algorithm together)
Thanks!
You should paste your source code.Then your test data format should be consistent with your train data including the feature name.But you don't need label column.
You can refer to official site